arXiv Machine Learning June 24, 2026 By Chi-Sheng Chen, En-Jui Kuo

Quantum Adaptive Self-Attention for Quantum Transformer Models

Read the original on arXiv Machine Learning →

arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 8

Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks

arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.

By Chi-Sheng Chen, En-Jui Kuo

llms benchmarks

arXiv AI

Aug 10

Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification

arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.

By Hao-Yuan Chen

llms

Hugging Face Trending Papers

Jul 14

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.

llms nlp fine-tuning

arXiv Machine Learning

Jul 1

Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning

arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.

By Kung-Ming Lan

rag safety

arXiv AI

Jul 15

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

arXiv:2607. 12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates.

By Mehdi Saeedi, Eddie Richter, Paul Hartke

llms nlp fine-tuning

arXiv Machine Learning

Jul 30

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.

By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov

rag benchmarks

Related stories

Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks

Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks